Автор: Marius Leordeanu Название: Unsupervised Learning in Space and Time ISBN: 3030421279 ISBN-13(EAN): 9783030421274 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video.
The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Автор: Ranjan Sumit, Senthamilarasu S. Название: Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques ISBN: 1838646302 ISBN-13(EAN): 9781838646301 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars. You will use real-world examples to implement various neural network architectures to develop your own autonomous and automated vehicle using the Python environment.
Автор: Ma, Yao (michigan State University) Tang, Jiliang (michigan State University) Название: Deep learning on graphs ISBN: 1108831745 ISBN-13(EAN): 9781108831741 Издательство: Cambridge Academ Рейтинг: Цена: 47510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, and scalability and then demonstrates applications in NLP, data mining, vision and healthcare.
Автор: Kar Krishnendu Название: Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques ISBN: 1838827064 ISBN-13(EAN): 9781838827069 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.
Автор: Ansari Shamshad Название: Building Computer Vision Applications Using Artificial Neural Networks: With Step-By-Step Examples in Opencv and Tensorflow with Python ISBN: 148425886X ISBN-13(EAN): 9781484258866 Издательство: Springer Рейтинг: Цена: 30740.00 T Наличие на складе: Невозможна поставка. Описание: Section 11. Chapter 1: Prerequisite and Software Installation 1.1. Python and PIP 1.1.1. Installing Python and PIP on Ubuntu 1.1.2. Installing Python and PIP on Mac OS 1.1.3. Installing Python and PIP on CentOS 7 1.1.4. Installing Python and PIP on Windows 1.2. Virtualenv 1.2.1. Setup and activate virtualenv 1.3. Tensorflow 1.3.1. Installing Tensorflow 1.4. PyCharm IDE 1.4.1. Installing PyCharm 1.4.2. Configuring PyCharm to use virtualenv 1.5. OpenCV 1.5.1. Installing OpenCV 1.5.2. Installing OpenCV4 with Python bindings 1.6. Additional libraries 1.6.1. SciPy 1.6.2. Matplotlib Chapter 2: Core Concepts of Image and Video Processing 1.7. Image processing 1.7.1. Image basics 1.7.2. Pixel 1.7.3. Pixel color 1.7.3.1. Grayscale 1.7.3.2. Color 1.7.4. Coordinate system 1.7.5. Python and OpenCV code to manipulate images 1.7.6. Program: loading, exploring and showing image 1.7.7. Program: OpenCV code to access and manipulate pixels 1.8. Drawing 1.8.1. Drawing a line on an image 1.8.2. Drawing a rectangle on an image 1.8.3. Drawing a circle on an image 1.9. Chapter summary 1.10. 2. Chapter 3: Techniques of Image Processing 2.1. Transformation 2.1.1. Resizing 2.1.2. Translation 2.1.3. Rotation 2.1.4. Flipping 2.1.5. Cropping 2.2. Image arithmetic and bitwise operations 2.2.1. Addition 2.2.2. Subtraction 2.2.3. Bitwise operations 2.2.3.1. OR 2.2.3.2. AND 2.2.3.3. NOT 2.2.3.4. XOR 2.3. Masking 2.4. Splitting and merging channels 2.5. Smoothing and blurring 2.6. Thresholding 2.7. Gradient and edge detection 2.8. Contours2.9. Chapter summary Section 23. Chapter 4: Building Artificial Intelligence System For Computer Vision 3.1. Image processing pipeline 3.2. Feature extraction 3.2.1. Color histogram 3.2.2. GLCM 3.2.3. HOG 3.2.4. LBP 3.3. Feature selection 3.3.1. Filter 3.3.2. Wrapper 3.3.3. Embedded 3.3.4. Regularization 3.4. Chapter summary 4. Chapter 5: Artificial Neural Network for Computer Vision 4.1. Introduction to ANN 4.1.1. ANN topology 4.1.2. Hyperparameters 4.1.3. ANN model training using TensorFlow 4.1.4. Model evaluation 4.1.5. Model deployment 4.1.6. Use of trained model 4.2. Introduction to Convolution Neural Network (CNN)4.2.1. Core concepts of CNN4.2.2. Creating training set for CNN4.2.3. Training CNN model using TensorFlow 4.2.4. Inspecting CNN model and evaluating model fitness4.2.5. Using and deployment of trained model4.3. Introduction to Recurrent Neural Network (RNN) and long short-term Memory (LSTM)4.3.1. Core concepts of RNN and LSTM4.3.2. Creating training set for LSTM4.3.3. LSTM model training using TensorFlow4.3.4. Inspecting LSTM model and assessing fitness4.3.5. Deploying LSTM models in practice Section 35. Chapter 6: Practical Example 1- Object Detection in Images 6. Chapter 7: Practical Example 2- Object Tracking in Videos 7. Chapter 8: Practical Example 3- Facial Detection 8. Chapter 9: Industrial Application - Realtime Defect Detection in Industrial Manufacturing
Автор: Le Lu; Yefeng Zheng; Gustavo Carneiro; Lin Yang Название: Deep Learning and Convolutional Neural Networks for Medical Image Computing ISBN: 3319827138 ISBN-13(EAN): 9783319827131 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Поставка под заказ. Описание: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Автор: Sathiyamoorthi Velayutham Название: Handbook of Research on Applications and Implementations of Machine Learning Techniques ISBN: 1522599029 ISBN-13(EAN): 9781522599029 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 264270.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.
The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.
Автор: Lina Yao, Xiang Zhang Название: Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications ISBN: 1786349582 ISBN-13(EAN): 9781786349583 Издательство: World Scientific Publishing Рейтинг: Цена: 95040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Deep Learning for EEG-based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI).
Автор: Verdhan Vaibhav Название: Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras ISBN: 1484266153 ISBN-13(EAN): 9781484266151 Издательство: Springer Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1 Introduction to Computer Vision and Deep Learning.- Chapter 2 Nuts and Bolts of Deep Learning for Computer Vision.- Chapter 3 Image Classification using LeNet.- Chapter 4 VGGNet and AlexNext Networks.- Chapter 5 Object Detection Using Deep Learning.- Chapter 6 Facial Recognition and Gesture Recognition.- Chapter 7 Video Analytics Using Deep Learning.- Chapter 8 End-to-end Model Development.- Appendix.
Автор: Concha Bielza, Pedro Larranaga Название: Data-Driven Computational Neuroscience: Machine Learning and Statistical Models ISBN: 110849370X ISBN-13(EAN): 9781108493703 Издательство: Cambridge Academ Рейтинг: Цена: 85530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods.
Автор: Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun Название: A Guide to Convolutional Neural Networks for Computer Vision ISBN: 1681732785 ISBN-13(EAN): 9781681732787 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 102570.00 T Наличие на складе: Невозможна поставка. Описание: Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision.This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation.This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
Казахстан, 010000 г. Астана, проспект Туран 43/5, НП2 (офис 2) ТОО "Логобук" Тел:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
